SoroushVahidi/scidocs huggingface dataset:
Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization
(2017)

This work studies how an AI-controlled dog-fighting agent with tunable decisionmaking parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima. These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multipoint Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.
Folded cascode OTA design for wide band applications
(2006)

This paper deals with design and optimization of a folded cascode operational transconductance amplifier. First, a detailed description of an optimum OTA topology is done in order to optimize MOS transistor sizing. Second, the design of folded cascode OTA, which works for frequencies that lead to a base band circuit design for RF application, is based on transistor sizing methodology. Third, folded cascode OTAs generally find several applications that are well developed. Simulation results are performed using SPICE software and BSIM3V3 model for CMOS 0.35mum process, show that the designed folded cascode OTA has a 85dB DC gain and provides a gain bandwidth product of around 332MHz
On visual similarity based 2D drawing retrieval
(2006)

A large amount of 2D drawings have been produced in engineering fields. To reuse and share the available drawings efficiently, we propose two methods in this paper, namely 2.5D spherical harmonics transformation and 2D shape histogram, to retrieve 2D drawings by measuring their shape similarity. The first approach represents a drawing as a spherical function by transforming it from a 2D space into a 3D space. Then a fast spherical harmonics transformation is employed to get a rotation invariant descriptor. The second statistics-based approach represents the shape of a 2D drawing using a distance distribution between two randomly sampled points. To allow users to interactively emphasize certain local shapes that they are interested in, we have adopted a flexible sampling strategy by specifying a bias sampling density upon these local shapes. The two proposed methods have many valuable properties, including transform invariance, efficiency, and robustness. In addition, their insensitivity to noise allows for the user’s causal input, thus supporting a freehand sketch-based retrieval user interface. Experiments show that a better performance can be achieved by combining them together using weights. q 2005 Elsevier Ltd. All rights reserved.
Paraphrasing for Style
(2012)

We present initial investigation into the task of paraphrasing language while targeting a particular writing style. The plays of William Shakespeare and their modern translations are used as a testbed for evaluating paraphrase systems targeting a specific style of writing. We show that even with a relatively small amount of parallel training data, it is possible to learn paraphrase models which capture stylistic phenomena, and these models outperform baselines based on dictionaries and out-of-domain parallel text. In addition we present an initial investigation into automatic evaluation metrics for paraphrasing writing style. To the best of our knowledge this is the first work to investigate the task of paraphrasing text with the goal of targeting a specific style of writing.
Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads †
(2012)

Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications) and UMTS (Universal Mobile Telecommunications System) base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.
Routing questions to appropriate answerers in community question answering services
(2010)

Community Question Answering (CQA) service provides a platform for increasing number of users to ask and answer for their own needs but unanswered questions still exist within a fixed period. To address this, the paper aims to route questions to the right answerers who have a top rank in accordance of their previous answering performance. In order to rank the answerers, we propose a framework called Question Routing (QR) which consists of four phases: (1) performance profiling, (2) expertise estimation, (3) availability estimation, and (4) answerer ranking. Applying the framework, we conduct experiments with Yahoo! Answers dataset and the results demonstrate that on average each of 1,713 testing questions obtains at least one answer if it is routed to the top 20 ranked answerers.
Punctuation Prediction with Transition-based Parsing
(2013)

Punctuations are not available in automatic speech recognition outputs, which could create barriers to many subsequent text processing tasks. This paper proposes a novel method to predict punctuation symbols for the stream of words in transcribed speech texts. Our method jointly performs parsing and punctuation prediction by integrating a rich set of syntactic features when processing words from left to right. It can exploit a global view to capture long-range dependencies for punctuation prediction with linear complexity. The experimental results on the test data sets of IWSLT and TDT4 show that our method can achieve high-level performance in punctuation prediction over the stream of words in transcribed speech text.
A circularly polarized planar antenna modified for passive UHF RFID
(2009)

The majority of RFID tags are linearly polarized dipole antennas, but a few use a planar, dual-dipole antenna that facilitates circular polarization, but requires a three-terminal IC. In this paper, we present a novel way to achieve circular polarization with a planar antenna using a two-terminal IC. We present an intuitive methodology for design, and perform experiments that validate circular polarization. The results show that the tag exhibits strong circular polarization, but the precise axial ratio of the tag remains uncertain due to lack of precision in the experimental system.
The art of UHF RFID antenna design: impedance-matching and size-reduction techniques
(2008)

Radio-frequency identification technology, based on the reader/tag paradigm, is quickly permeating several aspects of everyday life. The electromagnetic research mainly concerns the design of tag antennas having high efficiency and small size, and suited to complex impedance matching to the embedded electronics. Starting from the available but fragmented open literature, this paper presents a homogeneous survey of relevant methodologies for the design of UHF passive tag antennas. Particular care is taken to illustrate, within a common framework, the basic concepts of the most-used design layouts. The design techniques are illustrated by means of many noncommercial examples.
Novel Compact Circularly Polarized Square Microstrip Antenna
(2001)

A novel compact circular-polarization (CP) operation of the square microstrip antenna with four slits and a pair of truncated corners is proposed and investigated. Experimental results show that the proposed compact CP design can have an antenna-size reduction of about 36% as compared to the conventional corner-truncated square microstrip antenna at a given operating frequency. Also, the required size of the truncated corners for CP operation is much greater than that for the conventional CP design using a simple square microstrip patch, providing a relaxed manufacturing tolerance for the proposed compact CP design. Details of the experimental results are presented and discussed.
The history of RFID
(2005)

Radio frequency identification (RFID) is an integral part of our life, which increases productivity and convenience. It is the term coined for short-range radio technology used to communicate mainly digital information between a stationary location and a movable object or between movable objects. This RFID system uses the principle of modulated backscatter where it can transfer the data from the tag to the reader. The tag generally reads its internal memory of stored data and changes the loading on the tag antenna in a coded manner corresponding to the stored data. RFID is a technology, which spans systems engineering, software development, encryption etc., and thus there are many engineers involved in the development and application of RFID and at present the shortage of technical and business people trained in RFID is hampering the growth of the industry.
Software Cost Estimation Meets Software Diversity
(2017)

The previous goal of having a one-size-fits-all software cost (and schedule) estimation model is no longer achievable. Sources of wide variation in the nature of software development and evolution processes, products, properties, and personnel (PPPPs) require a variety of estimation models and methods best fitting their situations. This Technical Briefing will provide a short history of pattern-breaking changes in software estimation methods, a summary of the sources of variation in software PPPPs and their estimation implications, a summary of the types of estimation methods being widely used or emerging, a summary of the best estimation-types for the various PPPP-types, and a process for guiding an organization's choices of estimation methods as their PPPP-types evolve.
Balance equilibrium manifold and control of rider-bikebot systems
(2016)

Postural balance control of machines is one of the most important human motor skills. We present a balance equilibrium manifold (BEM) concept to study how a human rider balances a bicycle-like robot (i.e., bikebot) while maintaining tracking a desired trajectory. The BEM is built on the external-internal convertible (EIC) structure of the rider-bikebot dynamics to capture the balance properties of the rider-bikebot systems. We analyze the contribution of the upper-body movements and the steering inputs to the task of balance control of the systems. The results reveal that steering actuation is more effective than upper-body movement for balance task. A performance metric is also introduced to quantify the balance motor skills using the BEM. Extensive experiments are conducted to validate and demonstrate the analyses and the proposed balance skill metrics.
The Adaptive Markets Hypothesis Market efficiency from an evolutionary perspective
(2004)

30TH ANNIVERSARY ISSUE 2004 THE JOURNAL OF PORTFOLIO MANAGEMENT 15 The 30th anniversary of The Journal of Portfolio Management is a milestone in the rich intellectual history of modern finance, firmly establishing the relevance of quantitative models and scientific inquiry in the practice of financial management. One of the most enduring ideas from this intellectual history is the Efficient Markets Hypothesis (EMH), a deceptively simple notion that has become a lightning rod for its disciples and the proponents of behavioral economics and finance. In its purest form, the EMH obviates active portfolio management, calling into question the very motivation for portfolio research. It is only fitting that we revisit this groundbreaking idea after three very successful decades of this Journal. In this article, I review the current state of the controversy surrounding the EMH and propose a new perspective that reconciles the two opposing schools of thought. The proposed reconciliation, which I call the Adaptive Markets Hypothesis (AMH), is based on an evolutionary approach to economic interactions, as well as some recent research in the cognitive neurosciences that has been transforming and revitalizing the intersection of psychology and economics. Although some of these ideas have not yet been fully articulated within a rigorous quantitative framework, long time students of the EMH and seasoned practitioners will no doubt recognize immediately the possibilities generated by this new perspective. Only time will tell whether its potential will be fulfilled. I begin with a brief review of the classic version of the EMH, and then summarize the most significant criticisms leveled against it by psychologists and behavioral economists. I argue that the sources of this controversy can ANDREW W. LO is Harris & Harris Group Professor at the MIT Sloan School of Management, and chief scientific officer at AlphaSimplex Group, LLC, in Cambridge, MA. The Adaptive Markets Hypothesis
The distance function effect on k-nearest neighbor classification for medical datasets
(2016)

INTRODUCTION
K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output.


CASE DESCRIPTION
Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually.


DISCUSSION AND EVALUATION
The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets.


CONCLUSIONS
In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.
Application of Gradient Boosting through SAS Enterprise Miner TM 12 . 3 to Classify Human Activities
(2016)

Using smart clothing with wearable medical sensors integrated to keep track of human health is now attracting many researchers. However, body movement caused by daily human activities inserts artificial noise into physiological data signals, which affects the output of a health monitoring/alert system. To overcome this problem, recognizing human activities, determining relationship between activities and physiological signals, and removing noise from the collected signals are essential steps. This paper focuses on the first step, which is human activity recognition. Our research shows that no other study used SAS® for classifying human activities. For this study, two data sets were collected from an open repository. Both data sets have 561 input variables and one nominal target variable with four levels. Principal component analysis along with other variable reduction and selection techniques were applied to reduce dimensionality in the input space. Several modeling techniques with different optimization parameters were used to classify human activity. The gradient boosting model was selected as the best model based on a test misclassification rate of 0.1233. That is, 87.67% of total events were classified correctly.
Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies.
(2005)

Polyphenols are abundant micronutrients in our diet, and evidence for their role in the prevention of degenerative diseases is emerging. Bioavailability differs greatly from one polyphenol to another, so that the most abundant polyphenols in our diet are not necessarily those leading to the highest concentrations of active metabolites in target tissues. Mean values for the maximal plasma concentration, the time to reach the maximal plasma concentration, the area under the plasma concentration-time curve, the elimination half-life, and the relative urinary excretion were calculated for 18 major polyphenols. We used data from 97 studies that investigated the kinetics and extent of polyphenol absorption among adults, after ingestion of a single dose of polyphenol provided as pure compound, plant extract, or whole food/beverage. The metabolites present in blood, resulting from digestive and hepatic activity, usually differ from the native compounds. The nature of the known metabolites is described when data are available. The plasma concentrations of total metabolites ranged from 0 to 4 mumol/L with an intake of 50 mg aglycone equivalents, and the relative urinary excretion ranged from 0.3% to 43% of the ingested dose, depending on the polyphenol. Gallic acid and isoflavones are the most well-absorbed polyphenols, followed by catechins, flavanones, and quercetin glucosides, but with different kinetics. The least well-absorbed polyphenols are the proanthocyanidins, the galloylated tea catechins, and the anthocyanins. Data are still too limited for assessment of hydroxycinnamic acids and other polyphenols. These data may be useful for the design and interpretation of intervention studies investigating the health effects of polyphenols.
Quantum Algorithm Implementations for Beginners
(2018)

Patrick J. Coles, Stephan Eidenbenz,∗ Scott Pakin, Adetokunbo Adedoyin, John Ambrosiano, Petr Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Andrey Lokhov, Alexander Malyzhenkov, David Mascarenas, Susan Mniszewski, Balu Nadiga, Dan O’Malley, Diane Oyen, Lakshman Prasad, Randy Roberts, Phil Romero, Nandakishore Santhi, Nikolai Sinitsyn, Pieter Swart, Marc Vuffray, Jim Wendelberger, Boram Yoon, Richard Zamora, and Wei Zhu Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Alana: Social Dialogue using an Ensemble Model and a Ranker trained on User Feedback
(2017)

We describe our Alexa prize system (called ‘Alana’) which consists of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose system responses. This paper reports on the version of the system developed and evaluated in the semi-finals of the competition (i.e. up to 15 August 2017), but not on subsequent enhancements. The ranker for this system was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition. In order to avoid initial problems of inappropriate and boring utterances coming from big datasets such as Reddit and Twitter, we later focussed on ‘clean’ data sources such as news and facts. We report on experiments with different ranking functions and versions of our NewsBot. We find that a multiturn news strategy is beneficial, and that a ranker trained on the ratings feedback from users is also effective. Our system continuously improved using the data gathered over the course over the competition (1 July – 15 August) . Our final user score (averaged user rating over the whole semi-finals period) was 3.12, and we achieved 3.3 for the averaged user rating over the last week of the semi-finals (8-15 August 2017). We were also able to achieve long dialogues (average 10.7 turns) during the competition period. In subsequent weeks, after the end of the semi-final competition, we have achieved our highest scores of 3.52 (daily average, 18th October), 3.45 (weekly average on 23 and 24 October), and average dialogue lengths of 14.6 turns (1 October), and median dialogue length of 2.25 minutes (average for 7 days on 10th October).
CBAM: Convolutional Block Attention Module
(2018)

We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement. Because CBAM is a lightweight and general module, it can be integrated into any CNN architectures seamlessly with negligible overheads and is end-to-end trainable along with base CNNs. We validate our CBAM through extensive experiments on ImageNet-1K, MS COCO detection, and VOC 2007 detection datasets. Our experiments show consistent improvements in classification and detection performances with various models, demonstrating the wide applicability of CBAM. The code and models will be publicly available.
Use of assistance and therapy dogs for children with autism spectrum disorders: a critical review of the current evidence.
(2013)

BACKGROUND
Autism spectrum disorders (ASD) are characterized by deficits in social reciprocity and communication, and by unusually restricted, repetitive behaviors. Intervention strategies based on the exploitation of the emotional aspects of human-dog relationships hold the potential to overcome the difficulty of subjects with ASD to relate and interact effectively with others, targeting core symptoms of this disorder.


METHODS
This review summarizes the results of six published studies on the effects of brief interactions with dogs and the effects of introducing dogs in families with a child diagnosed with ASD, with an emphasis on social behaviors and language use. Furthermore, the possible mechanisms responsible for the beneficial effects observed are discussed.


CONCLUSIONS
Although the studies described here are encouraging, further research with better designs and using larger samples is needed to strengthen translation of such interventions to the clinic. In addition, potential applications of analyzing child-dog interactions are highlighted to screen for early signs of the disorder.